Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation

• URL: http://arxiv.org/abs/2409.11535v1
• Date: Tue, 17 Sep 2024 20:13:32 GMT
• Title: Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation
• Authors: Michael Lingzhi Li, Shixiang Zhu,
• Abstract summary: We introduce a novel framework called generative curation, which optimize the true desirability of decision options by integrating both quantitative and qualitative aspects. We propose two implementation approaches: a generative neural network architecture that produces a distribution $pi$ to efficiently sample a diverse set of near-optimal actions, and a sequential optimization method to iteratively generate solutions. We validate our approach with extensive datasets, demonstrating its effectiveness in enhancing decision-making processes across a range of complex environments.
• Score: 6.980546503227467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The surge in data availability has inundated decision-makers with an overwhelming array of choices. While existing approaches focus on optimizing decisions based on quantifiable metrics, practical decision-making often requires balancing measurable quantitative criteria with unmeasurable qualitative factors embedded in the broader context. In such cases, algorithms can generate high-quality recommendations, but the final decision rests with the human, who must weigh both dimensions. We define the process of selecting the optimal set of algorithmic recommendations in this context as human-centered decision making. To address this challenge, we introduce a novel framework called generative curation, which optimizes the true desirability of decision options by integrating both quantitative and qualitative aspects. Our framework uses a Gaussian process to model unknown qualitative factors and derives a diversity metric that balances quantitative optimality with qualitative diversity. This trade-off enables the generation of a manageable subset of diverse, near-optimal actions that are robust to unknown qualitative preferences. To operationalize this framework, we propose two implementation approaches: a generative neural network architecture that produces a distribution $\pi$ to efficiently sample a diverse set of near-optimal actions, and a sequential optimization method to iteratively generates solutions that can be easily incorporated into complex optimization formulations. We validate our approach with extensive datasets, demonstrating its effectiveness in enhancing decision-making processes across a range of complex environments, with significant implications for policy and management.

Related papers

• Towards Robust Interpretable Surrogates for Optimization [0.0]
  An important factor in the practical implementation of optimization models is the acceptance by the intended users. We present suitable models based on different variants to model uncertainty, and solution methods.
  arXiv  Detail & Related papers  (2024-12-02T08:31:48Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization [9.838618121102053]
  In real-world applications, users often favor structurally diverse design choices over one high-quality solution. This paper considers the problem of identifying a fixed number of solutions with a pairwise distance above a specified threshold. We analyze how this trade-off depends on the properties of the underlying optimization problem.
  arXiv  Detail & Related papers  (2024-08-29T09:55:55Z)
• End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty [55.04219793298687]
  The Predict-Then-Forecast (PtO) paradigm in machine learning aims to maximize downstream decision quality. This paper extends the PtO methodology to optimization problems with nondifferentiable Ordered Weighted Averaging (OWA) objectives. It shows how optimization of OWA functions can be effectively integrated with parametric prediction for fair and robust optimization under uncertainty.
  arXiv  Detail & Related papers  (2024-02-12T16:33:35Z)
• A new fuzzy multi-attribute group decision-making method based on TOPSIS and optimization models [3.697049647195136]
  A new method is proposed for multi-attribute group decision-making in interval-valued intuitionistic fuzzy sets. By minimizing the sum of differences between individual evaluations and the overallconsistent evaluations of all experts, a new optimization model is established for determining expert weights. The complete fuzzy multi-attribute group decision-making algorithm is formulated, which can give full play to the advantages of subjective and objective weighting methods.
  arXiv  Detail & Related papers  (2023-11-27T15:41:30Z)
• Predict-Then-Optimize by Proxy: Learning Joint Models of Prediction and Optimization [59.386153202037086]
  Predict-Then- framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This approach can be inefficient and requires handcrafted, problem-specific rules for backpropagation through the optimization step. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by predictive models.
  arXiv  Detail & Related papers  (2023-11-22T01:32:06Z)
• Bayesian Quality-Diversity approaches for constrained optimization problems with mixed continuous, discrete and categorical variables [0.3626013617212667]
  A new Quality-Diversity methodology based on mixed variables is proposed in the context of limited simulation budget. The proposed approach provides valuable trade-offs for decision-markers for complex system design.
  arXiv  Detail & Related papers  (2023-09-11T14:29:47Z)
• Robust Data-driven Prescriptiveness Optimization [4.792851066169871]
  This paper introduces a distributionally robust contextual optimization model where the coefficient of prescriptiveness substitutes for the classical empirical risk objective minimization. We evaluate the robustness of the resulting policies against alternative methods when the out-of-sample dataset is subject to varying amounts of distribution shift.
  arXiv  Detail & Related papers  (2023-06-09T14:56:06Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning [52.74071439183113]
  We study the predict-then-optimize framework in the context of sequential decision problems (formulated as MDPs) solved via reinforcement learning. Two significant computational challenges arise in applying decision-focused learning to MDPs.
  arXiv  Detail & Related papers  (2021-06-06T23:53:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.